Evidence map›Paper›PMID 37495257›Full record

ArticleBMJ open quality2023

Data-driven approach to identifying potential laboratory overuse in general internal medicine (GIM) inpatients.

Adina S Weinerman, Yishan Guo, Sudipta Saha, Paul M Yip, Lauren Lapointe-Shaw, Michael Fralick, Janice L Kwan, Thomas E MacMillan, Jessica Liu, Shail Rawal and 6 more

Abstract read
In one paragraph

Article in BMJ open quality, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Adina S WeinermanDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada Adina.Weinerman@sunnybrook.ca.ORCID 0000-0002-5527-7619
Yishan GuoLi Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada.
Sudipta SahaLi Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada.
Paul M YipPrecision Diagnostics and Therapeutics Program, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.ORCID 0000-0002-7635-7407
Lauren Lapointe-ShawDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.
Michael FralickDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-2082-2445
Janice L KwanDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.
Thomas E MacMillanDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.
Jessica LiuDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0003-1545-1560
Shail RawalDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-8466-1193
Kathleen A SheehanDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-8700-0809
Janet SimonsDepartment of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, Ontario, Canada.
Terence TangDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.
Sacha BhatiaDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.
Fahad RazakDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.
Amol A VermaDepartment of Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-6857-931X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReducing laboratory test overuse is important for high quality, patient-centred care. Identifying priorities to reduce low value testing remains a challenge.

objectiveTo develop a simple, data-driven approach to identify potential sources of laboratory overuse by combining the total cost, proportion of abnormal results and physician-level variation in use of laboratory tests. DESIGN, SETTING AND

participantsA multicentre, retrospective study at three academic hospitals in Toronto, Canada. All general internal medicine (GIM) hospitalisations between 1 April 2010 and 31 October 2017.

resultsThere were 106 813 GIM hospitalisations during the study period, with median hospital length-of-stay of 4.6 days (IQR: 2.33-9.19). There were 21 tests which had a cumulative cost >US$15 400 at all three sites. The costliest test was plasma electrolytes (US$4 907 775), the test with the lowest proportion of abnormal results was red cell folate (0.2%) and the test with the greatest physician-level variation in use was antiphospholipid antibodies (coefficient of variation 3.08). The five tests with the highest cumulative rank based on greatest cost, lowest proportion of abnormal results and highest physician-level variation were: (1) lactate, (2) antiphospholipid antibodies, (3) magnesium, (4) troponin and (5) partial thromboplastin time. In addition, this method identified unique tests that may be a potential source of laboratory overuse at each hospital.

conclusionsA simple multidimensional, data-driven approach combining cost, proportion of abnormal results and physician-level variation can inform interventions to reduce laboratory test overuse. Reducing low value laboratory testing is important to promote high value, patient-centred care.

Indexed as

InpatientsPhysiciansHospitalizationHumansInternal MedicineRetrospective StudiesHealthcare quality improvementLaboratory medicineQuality improvement

Identifiers

PMID37495257
PMCPMC10373691

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.